sboost: Machine Learning with AdaBoost on Decision Stumps

Creates classifier for binary outcomes using Adaptive Boosting
(AdaBoost) algorithm on decision stumps with a fast C++ implementation.
For a description of AdaBoost, see Freund and Schapire (1997)
<doi:10.1006/jcss.1997.1504>. This type of classifier is nonlinear, but
easy to interpret and visualize. Feature vectors may be a combination of
continuous (numeric) and categorical (string, factor) elements. Methods
for classifier assessment, predictions, and cross-validation also included.